Classification results for the baseline classifier (top row) and a classifier with signal history included in the feature space (bottom row).

<p>The rectangular blue waveform indicates the onset and offset of finger tapping. A,C) The time series of oxy-Hb concentration change in channel 13 in participant 1 is plotted. The time points which are classified as active are plotted in red, and inactive in black. B,D) The time series and classification result of trial 11 is shown in more detail. The feature space of the baseline classifier is simply the amplitude of oxy-Hb in channel 13 (i.e. one dimensional), which essentially classifies based on a single threshold. It's evident from panels A and B that the classified active state (red) is delayed from the true active state (between the vertical blue lines). The onset classification delay in trial 11 is about 6s, and the offset delay is 1.6s. With 2s history of the oxy-Hb signal in channel 13 incorporated into the feature space (C and D, 21-dimensional feature space), the delay is reduced to 4s (onset) and 0.2s (offset).</p

Including first and second order gradients doesn't improve classification performance.

<p>A) accuracy, B) onset delay, and C) offset delay are nearly identical with classifiers based on amplitude, and first or second order gradients.</p

Classification results with feature spaces including oxy-Hb only (blue), deoxy-Hb only (green), both oxy-Hb and deoxy-Hb (red), CBSI corrected oxy-Hb (cyan), and total (oxy-Hb+deoxy-Hb, pink).

<p>A) accuracy, B) onset delay, and C) offset delay. Compared to the oxy-Hb only feature space, incorporating both oxy-Hb and deoxy-Hb improves the accuracy by 2.4% and reduces the onset delay by 0.3s. The total Hb signal gave the worst classification results; due to poor accuracy, we did not calculate the onset and offset delay for total-Hb.</p

Including signals from multiple channels into the feature space improves accuracy and reduces delay in participant 1.

<p>A) accuracy, B) onset delay, and C) offset delay. We ordered all 48 channels by decreasing CNR, and then included one additional channel in each round in that order. For this participant the accuracy peaked at 9 channels and then declined due to overfitting. With 9 channels, the onset delay is reduced to 1.2s and the offset delay is reduced to 0.7s.</p

Classification results for all participants.

<p>A) accuracy, B) onset delay C) offset delay. The largest boost in accuracy and drop in delay occurs when history is included in the feature space. For some participants (#1 and #6), including deoxy-Hb or including signals from other channels also improved the performance. Comparing the classifier with the full feature space (history of oxy-Hb and deoxy-Hb, and other channels) to the baseline classifier, the average increase in accuracy is 7.7% (p = 0.004, one sample T test, degree of freedom = 5), the average reduction in onset delay is 2.4s (p = 0.02), and the average reduction in offset delay is 1.3s (p = 0.003).</p

Table_1_Oral microbiome characteristics in patients with pediatric solid tumor.DOC

BackgroundPediatric solid tumor, the abnormal proliferation of solid tissues in children resulting in the formation of tumors, represent a prevailing malignant ailment among the younger population. Extensive literature highlights the inseparable association linking oral microbiome and adult tumors, but due to differences in age of onset, characteristics of onset, etc., there are many differences between Pediatric solid tumors and adult tumors, and therefore, studying the relationship between Pediatric solid tumor and the oral microbiota is also essential.MethodsTo unravel the distinct characteristics of the oral microbiota within Pediatric solid tumor patients, 43 saliva samples, encompassing 23 Pediatric solid tumor patients and 20 healthy controls, were diligently procured. A meticulous screening process ensued, and conducted microbial MiSeq sequencing after screening.ResultsWe documented the oral microbiome attributes among pediatric diagnosed with solid tumors (PST), and meanwhile, we observed a significant trend of decreased oral microbiota diversity in the pediatric solid tumor group. There were notable disparities in microbial communities observed between the two groups, 18 genera including Veillonellaceae, Firmicutes unclassified, Coriobacteriia, Atopobiaceae, Negativicutes, were significantly enriched in PST patients, while 29 genera, including Gammaproteobacteria, Proteobacteria, Burkholderiales, Neisseriaceae, were dominant in the HCs group. It was found that PST group had 16 gene functions, including Amino acid metabolism, Cysteine and methionine metabolism, Photosynthesis antenna proteins, Arginine and proline metabolism, and Aminoacyl tRNA biosynthesi, were significantly dominant, while 29 gene functions that prevailed in HCs.ConclusionThis study characterized the oral microbiota of Pediatric solid tumor patients for the first time, and importantly, targeted biomarkers of oral microbiota may serve as powerful and non-invasive diagnostic tools for pediatric solid tumor patients.</p

Table_2_Oral microbiome characteristics in patients with pediatric solid tumor.XLSX

BackgroundPediatric solid tumor, the abnormal proliferation of solid tissues in children resulting in the formation of tumors, represent a prevailing malignant ailment among the younger population. Extensive literature highlights the inseparable association linking oral microbiome and adult tumors, but due to differences in age of onset, characteristics of onset, etc., there are many differences between Pediatric solid tumors and adult tumors, and therefore, studying the relationship between Pediatric solid tumor and the oral microbiota is also essential.MethodsTo unravel the distinct characteristics of the oral microbiota within Pediatric solid tumor patients, 43 saliva samples, encompassing 23 Pediatric solid tumor patients and 20 healthy controls, were diligently procured. A meticulous screening process ensued, and conducted microbial MiSeq sequencing after screening.ResultsWe documented the oral microbiome attributes among pediatric diagnosed with solid tumors (PST), and meanwhile, we observed a significant trend of decreased oral microbiota diversity in the pediatric solid tumor group. There were notable disparities in microbial communities observed between the two groups, 18 genera including Veillonellaceae, Firmicutes unclassified, Coriobacteriia, Atopobiaceae, Negativicutes, were significantly enriched in PST patients, while 29 genera, including Gammaproteobacteria, Proteobacteria, Burkholderiales, Neisseriaceae, were dominant in the HCs group. It was found that PST group had 16 gene functions, including Amino acid metabolism, Cysteine and methionine metabolism, Photosynthesis antenna proteins, Arginine and proline metabolism, and Aminoacyl tRNA biosynthesi, were significantly dominant, while 29 gene functions that prevailed in HCs.ConclusionThis study characterized the oral microbiota of Pediatric solid tumor patients for the first time, and importantly, targeted biomarkers of oral microbiota may serve as powerful and non-invasive diagnostic tools for pediatric solid tumor patients.</p

Table_3_Oral microbiome characteristics in patients with pediatric solid tumor.XLSX

BackgroundPediatric solid tumor, the abnormal proliferation of solid tissues in children resulting in the formation of tumors, represent a prevailing malignant ailment among the younger population. Extensive literature highlights the inseparable association linking oral microbiome and adult tumors, but due to differences in age of onset, characteristics of onset, etc., there are many differences between Pediatric solid tumors and adult tumors, and therefore, studying the relationship between Pediatric solid tumor and the oral microbiota is also essential.MethodsTo unravel the distinct characteristics of the oral microbiota within Pediatric solid tumor patients, 43 saliva samples, encompassing 23 Pediatric solid tumor patients and 20 healthy controls, were diligently procured. A meticulous screening process ensued, and conducted microbial MiSeq sequencing after screening.ResultsWe documented the oral microbiome attributes among pediatric diagnosed with solid tumors (PST), and meanwhile, we observed a significant trend of decreased oral microbiota diversity in the pediatric solid tumor group. There were notable disparities in microbial communities observed between the two groups, 18 genera including Veillonellaceae, Firmicutes unclassified, Coriobacteriia, Atopobiaceae, Negativicutes, were significantly enriched in PST patients, while 29 genera, including Gammaproteobacteria, Proteobacteria, Burkholderiales, Neisseriaceae, were dominant in the HCs group. It was found that PST group had 16 gene functions, including Amino acid metabolism, Cysteine and methionine metabolism, Photosynthesis antenna proteins, Arginine and proline metabolism, and Aminoacyl tRNA biosynthesi, were significantly dominant, while 29 gene functions that prevailed in HCs.ConclusionThis study characterized the oral microbiota of Pediatric solid tumor patients for the first time, and importantly, targeted biomarkers of oral microbiota may serve as powerful and non-invasive diagnostic tools for pediatric solid tumor patients.</p

DataSheet1_Enhanced non-linear optical properties of porphyrin-based polymers covalently functionalized with graphite phase carbon nitride.docx

In our work, a flurry of original porphyrin-based polymers covalently functionalized g-C3N4 nanohybrids were constructed and nominated as PPorx-g-C3N4 (x = 1, 2 and 3) through click chemistry between porphyrin-based polymers with alkyne end-groups [(PPorx-C≡CH (x = 1, 2 and 3)] and azide-functionalized graphitic carbon nitride (g-C3N4-N3). Due to the photoinduced electron transfer (PET) between porphyrin-based polymers [PPorx (x = 1, 2 and 3)] group and graphite phase carbon nitride (g-C3N4) group in PPorx-g-C3N4 nanohybrids, the PPorx-g-C3N4 nanohybrids exhibited better non-linear optical (NLO) performance than the corresponding PPorx-C≡CH and g-C3N4-N3. It found that the imaginary third-order susceptibility (Im [χ(3)]) value of the nanohybrids with different molecular weight (MW) of the pPorx group in the nanohybrids ranged from 2.5×103 to 7.0 × 103 g mol−1 was disparate. Quite interestingly, the Im [χ(3)] value of the nanohybrid with a pPorx group’s MW of 4.2 × 103 g mol−1 (PPor2-g-C3N4) was 1.47 × 10–10 esu, which exhibited the best NLO performance in methyl methacrylate (MMA) of all nanohybrids. The PPorx-g-C3N4 was dispersed in polymethyl methacrylate (PMMA) to prepare the composites PPorx-g-C3N4/PMMA since PMMA was widely used as an alternative to glass. PPor2-g-C3N4/PMMA showed the excellent NLO performance of all nanohybrids with the Im [χ(3)] value of 2.36 × 10–10 esu, limiting threshold of 1.71 J/cm2, minimum transmittance of 8% and dynamic range of 1.09 in PMMA, respectively. It suggested that PPorx-g-C3N4 nanohybrids were potential outstanding NLO materials.</p

Bispectral (BIS) value during the dexmedetomidine/propofol sedation procedure.

<p>Data are shown as the mean ± standard deviation. *<i>P<</i> 0.05, ***<i>P <</i>0.001. WA, wakefulness; MS, moderate sedation; DS, deep sedation; RS, recovery state (RS).</p